Andrew Chen's Recent LinkedIn Posts

Final reminder - last week to apply: A16Z SPEEDRUN ALPHA is for recent grads and college students who want to start a company but are pre-idea / pre-product details: - $20K equity-free upfront to start building - up to $250K investment when you finalize - automatic final interview for a16z speedrun, with up to $1M investment - 8-week, in-person experience with a kickoff retreat, founder AMAs, and small-group dinners alongside the a16z speedrun community - targeted to early-career highly technical founders We stayed Alpha bc its a time of great change in the startup community and in the job market. And we know that most of the best founders don’t start with a perfect idea. They start with curiosity, talent, and the willingness to build things until something clicks. Some of the most important companies of the last decade started this way: - tinkering with side projects - hacking on open source - building weird prototypes with friends - exploring a space before the opportunity was obvious The goal of Speedrun Alpha is simple: find exceptional builders before the idea is fully formed. Instead of asking you to show up with a polished pitch deck, we give you: - time - community - mentorship - and just enough capital to start experimenting. Just show up with technical ambition and curiosity. You spend the summer with us on the a16z Speedrun team exploring ideas, building prototypes, and talking to users. By the end, if something interesting emerges, we help you turn it into a real company. The kinds of founders we’re looking for tend to look like this: - engineers who can ship fast - builders who have shipped side projects before - hackers who like learning new systems quickly - people who would probably start companies eventually anyway We’re intentionally targeting recent grads and college students as a bet on the future. If that sounds like you — or someone you know — this is the last call.

I know I’m not the only one Mon-Fri: zoom zoom email email gsheet gdoc Fri-Sun: ssh tmux vi codex playwright openclaw npm claude git ollama cursor

software startup dot com startup Web 2.0 startup mobile-first startup AI-native startup … eventually becomes just “startup”

so i bought a USB foot pedal that triggers voice dictation for coding/email/whatever AMA

everyone claims they want "contrarian" founders but what they actually want - founders with a non-obvious insight that happens to align with an explosive market This is bc contrarian + right = visionary. contrarian + wrong = unemployed

Web 1.0 came with new channels: - email, search, link sharing, etc Web 2.0 too: - feeds, creators, viral invites, etc Mobile: - app stores, SMS invites, vertical vid, mobile ads What about AI? I’ve been complaining that AI hasn’t come with much. But we’re seeing a big growth channel opening now: Products that are built as APIs/CLIs that can be pulled into new projects by Codex/Claude on the fly Maybe the “AI-native hotel app” doesn’t mean a mobile booking app with an AI chat panel. It means a CLI that can book a hotel for you, that an AI agent can pull into a bespoke answer or project or into code. Bolting on an AI chat panel is this generation’s weak form of AI. Maybe the full reinvention involves making it agent-first not human-first and once you start looking at it that way, a lot of existing products suddenly feel mis-specified. they’re built as destinations, but agents don’t want destinations. they want capabilities. composable, callable, reliable capabilities. So instead of “go to Expedia” or “open the app,” the future interaction is more like: an agent assembles a workflow on the fly. it pulls a flight search tool, a hotel booking tool, maybe a weather model, maybe even your personal preference graph. none of these are full products in the traditional sense. they’re more like endpoints with taste and state. This flips distribution completely. historically you win by owning the surface area. seo, app store ranking, homepage traffic. in an agent world, you win by being the default callable primitive. the thing that shows up again and again in agent-generated plans because it works, has clean interfaces, and returns structured outputs. distribution shifts from “top of funnel” to “top of call stack.” This also suggests a new kind of moat. not just data or network effects, but integration depth with agent ecosystems. if claude or codex or openclaw learns that your tool is the safest way to accomplish X, it gets baked into prompts, templates, maybe even fine-tunes. you become a default. and defaults, historically, are insanely sticky. The contrarian take is that most current “AI features” are a local maximum. chat panels, copilots, assistants. they’re transitional. the real end state might look closer to invisible infrastructure that agents orchestrate. the ui is just a debug layer for humans to peek into what the agents are doing. so maybe the new growth channels for ai look like: - being callable - being composable - being reliable at scale in agent loops - being embedded in agent templates and workflows - being the default primitive in a given domain and if that’s right, then the question for any new product isn’t “what’s the ui” or even “what’s the killer feature.” it’s “what’s the minimal, highest-leverage capability we can expose such that agents will repeatedly choose us when building something new.”

Founder-Led Coding: Something that I think we’re about to see pretty often with the massive increase of entrepreneurial but non-technical founders who can use AI code gen to build their v1 products Founder led sales: this is where you just do all the selling, at the beginning, even if you’re not that good at it. Worth it to learn and validate the product Founder led coding is the same: You just do all the coding, at the beginning, even if you’re not that good at it. Worth it to learn and validate the product

Openclaw hang in Santa Monica at the a16z offices hosted by me and Chrys Bader Come join us!

AI is supposed to save me time, but now I find myself building stuff all evening and weekend and it's actually increasing my time in front of the computer WTF

prediction re the end of spreadsheets AI code gen means that anything that is currently modeled as a spreadsheet is better modeled in code. You get all the advantages of software - libraries, open source, AI, all the complexity and expressiveness. think about what spreadsheets actually are: they're business logic that's trapped in a grid. Pricing models, financial forecasts, inventory trackers, marketing attribution - these are all fundamentally *programs* that we've been writing in the worst possible IDE. No version control, no testing, no modularity. Just a fragile web of cell references that breaks when someone inserts a row. The only reason spreadsheets won is that the barrier to writing real software was too high. A finance analyst could learn =VLOOKUP in an afternoon but couldn't learn Python in a month. AI code gen flips that equation completely. Now the same analyst describes what they want in plain English, and gets a real application - with a database, a UI, error handling, the works. The marginal effort to go from "spreadsheet" to "software" just collapsed to near zero. this is a massive unlock. There are ~1 billion spreadsheet users worldwide. Most of them are building janky software without realizing it. When even 10% of those use cases migrate to actual code, you get an explosion of new micro-applications that look nothing like traditional software. Internal tools that used to live in a shared Google Sheet now become real products. The "shadow IT" spreadsheet that runs half the company's operations finally gets proper infrastructure. The interesting second-order effect: the spreadsheet was the great equalizer that let non-technical people build things. AI code gen is the *next* great equalizer, but the ceiling is 100x higher. We're about to see what happens when a billion knowledge workers can build real software.

the most important AI startups won't be the ones that replace humans... They'll be the ones that help humans figure out what to do next what will humans do next? I think we get a clue when it comes to the discussion around "taste" on one end of the spectrum and verifiability on the other AI is great at verifiable problems. Did the code compile, is the math right. Where it struggles: "was that email rude?" "does this tweet hit hard?" the gap between objectively verifiable and subjectively verifiable is where the most interesting companies will be built, because it's where humans can team up productively with AI

normie trad work: Do the job yourself 1st derivative: Use AI to help you do the job 2nd derivative: Teach AI to do the job for you 3rd derivative: Manage AIs that do the job 4th derivative: Design the AI systems that run the work 5th derivative: Do new work that only AI teams can do

the magic of cowork and openclaw and other AI products is that they replace our giant row of infinite browser tabs And lol - no, don't feel guilty, I have too many tabs too. AI makes it so that every workflow that required 4 browser tabs and a spreadsheet is getting collapsed into one AI-native experience Just as one quick example- think about how you used to research a person or a company: LinkedIn tab, X tab, Google tab, notes doc, slack open. now one prompt does it in 10 seconds. the "tab count" of a workflow is basically a proxy for how much AI can compress it if your product eliminates 6 tabs and a copy-paste loop, users will like it. If you can create a whole series of these workflows then your users will absolutely love it. Thus the biggest opportunities are workflows where people currently alt-tab 20+ times per task. Sales, recruiting, research, compliance, procurement. Boring? yes. Massive? also yes. But this is why these agentic tools are going to crush AI doesn't need to be superintelligent to be wildly useful. it just needs to be good enough to close the tabs

lots of AI cos starting to experiment with paid marketing so here’s my take: Paid acquisition is a tax on your product's defensibility. the moment you can't out-spend the incumbents and competitors, you die. build channels that get cheaper as you grow or you're just renting your growth

marketplace startups are destined to be massively reinvented by AI. The weak form is already happening, where we use LLMs for customer support, supply/demand matching, etc. That’s easy The strong form is to figure out how much of the supply side of the marketplace can be turned agentic and ultimately, robotic. “Uber for X” will have consumers requesting robots to do X. Every on-demand service of the 2010s will instruct a robotaxi or delivery robot. Or if you’re prev used a marketplace to hire X, then you “hire” an agent instead. You won’t need to app developer, because there’s agents to build your app This will impact marketplace cos differently. Of course some marketplaces - like Airbnb - inherently work in the physical and will leverage AI around the core value prop. And some are bound to lose their network effects as matching fragmented supply/demand turns into an AI problem. Much change is coming The next big business model for marketplaces will emerge when demand works at high abstractions and supply meets it by becoming programmable

when you're an individual contributor, when you stop, the work stops. But when you manage a whole bunch of agents and they're constantly pinging you because they finished their work, then congrats: You’re now a middle manager… for a team of agents It’s like having your calendar filling up with one-on-ones and OKRs and so on. The manager’s schedule. Except your one-on-ones are the moments where you give little prompts to the agents that you're ordering around. What a funny thing.

Insane amount of random projects over the past few weeks So many are half built but so much fun… Reply and share yours! - *book-reader* — Kindle-like audiobook web app with word-by-word highlighting synced to audio - *calscan* — Calendar intelligence CLI for Andrew's two Google calendars - *canon* — Beautiful reading room web app for humanity's most important free texts - *canon-library* — Curated collection of foundational texts powering Canon - *chief* — Email triage daemon — classifies, drafts replies, tracks delegations - *copycat-agent* — Multi-agent PLAN.md + PRD generator for product emulation - *counterpoint* — Multi-agent epistemic arena CLI (LLMs debate big questions) - *deckard* — One-shot consulting-style slide deck generator with multi-stage pipeline - *one-liner* — Multi-agent system for generating provocative tech aphorisms - *pplradar* — Relationship radar — "who should I be thinking about right now?" - *sprayprd* — Generates radically different PRDs from a single one-line product concept - *techhist* — Multi-agent essays exploring historical metaphors for current tech trends Openclaw skills: - *autobio* — Search/update Andrew's 350-month life history autobiography (1.14M words) - *autobio-diary* — Daily diary interview — asks Andrew questions about his day - *autobio-sparky* — Parse/refresh autobio raw sources on DGX Spark via Ollama - *calfollow* — Find recent calendar contacts and resolve identity for X follow-backs - *calscan* — Forward-looking milestones + backward-looking time analysis across 5 horizons - *chief* — Inbox triage skill - *daily-idea* — Pitch 3-5 actionable ideas based on goals, calendar, and context - *find-andrew-location* — Locate Andrew via iCloud Find My + calendar cross-check - *fro-photo* — Generate AI photos of Fro the pug on wild adventures - *gm* — Daily morning briefing (weather, calendar, email, news) - *integrations-self-test* — Comprehensive self-test for all channels and services - *meeting-prep* — Automated meeting briefings with attendee bios for external meetings - *ppl* — Relationship radar — 17K events, 9.9K people, 71K interactions scored - *random-cron* — Schedule messages/prompts to fire at random times - *reflect* — End-of-session self-improvement logging and skill audit - *saveclaw* — Push all critical repos to GitHub for disaster recovery - *skill-improver* — Full custom-skills audit with improvement ideas - *sparky* — Access/control NVIDIA DGX Spark over Tailscale - *sparky-health* — Fast health checks and incident triage on DGX Spark - *sr-pulse* — Speedrun portfolio company news monitoring and digest - *techvip* — Top AI/tech founders/CEOs/VCs list cross-referenced with X relationships - *todo-tracker* — Persistent TODO scratch pad across sessions - *topic-radar* — Surface trending topics relevant to Andrew's interests - *travel-news* — Weekly digest of news from places Andrew has traveled - *uber* — Book Uber rides via browser automation

in a world of agents, the product role is going to split into two jobs: - one that organizes humans (stakeholders, design, eng) - one that organizes agents (prompts, evals, workflows, etc) Both will be in pursuit of offering the right products to customers, but how you get there will dramatically change. What happens to the typical product rituals? Instead of PRDs, OKRs, standups, product reviews, we'll need the equivalent for agents. Couple wild ideas here... instead of standups: the equivalent is that agents will report back to us based on run logs and anomaly flags. no one needs to say what they did yesterday, the system already did thousands of things. the question is where it broke, where it surprised you, and where it got better. Show us the patterns, the trends, the edge cases - particularly the ones the agents didn't fix automatically. the daily ritual becomes reviewing deltas, scanning failures, and deciding which ones matter. less reporting, more triage instead of OKRs: we’ll need adversarial agents that continuously monitor/grade the system and detect patterns, scoring outcomes on an hourly or daily basis. Rather than setting a quarterly goal of "increase X by 5%" and revisiting slowly -- instead, management will be able to monitor success in real-time and detect trends/patterns towards overall goals instead of PRDs: we won't need waterfall. Prototyping will rule the day, and we’ll need a living agentic loop that mediates customer feedback/ratings and what's being prioritized and built. you don’t hand it to eng, you deploy it into the agent loop. if it’s wrong, it fails visibly and you can revert. if it’s right, it produces the right output instead of product reviews: we'll need simulation systems to examine agent behavior in different scenarios. In an agentic world where UI shifts from buttons/menus to agents automatically doing things, you'll want to examine their behavior before you deploy. You rewind decisions, fork alternate paths, and see how different prompts or constraints would have changed outcomes. the review becomes interactive. less storytelling, more counterfactuals. The PM sits in the middle of this split. On the human side, still aligning taste, risk tolerance, and strategy across people. On the agent side, shaping the actual behavior of the system through prompts, evals, and feedback loops. one side is persuasion. The other is instrumentation. the best ones will collapse the gap, translating intent directly into systems that act on it. the fascinating part is that the agentic loop will run 10000x faster than the human one, and of course, you can "hire" them faster. Thus the “organizing humans” half starts to feel slow and lower impact unless it directly improves the agent loop. Eventually the PM will shift towards agents and maybe ignore the human coordination altogether...

Tired: Universal basic income Wired: Universal basic compute

do agents dream of latent sheep?

👇🏼marketplace PMs - great role. Encourage you to reach out to Weber and the Flora team